Untitled

Cue only ! dummy coding

Pain cue

Pain :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0021.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0021.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 5.530679 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5785735  Bit rate: 22.46 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Pain :: check data coverage

m = mean(con_data_obj);
m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);
orthviews(m, 'trans') % display
SPM12: spm_check_registration (v7759)              14:47:45 - 01/06/2022
========================================================================
Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1
Grouping contiguous voxels:   1 regions
ans = 1×1 cell array
    {1×1 region}

Pain :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 25.00% Expected 3.00 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 6 36 47 Uncorrected: 5 images Cases 6 14 32 36 47 Retained 8 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 60.00% Expected 3.00 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 2 3.3333 global_mean_to_variance 1 1.6667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 3.3333 mahal_cov_uncor 5 8.3333 mahal_cov_corrected 3 5 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 8.3333 Overall_corrected 4 6.6667

SPM12: spm_check_registration (v7759) 14:48:08 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Pain :: run robfit

set(gcf,'Visible','on')

figure ('Visible', 'on');

drawnow, snapnow;

Pain :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
    %disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×56 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [56×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×56 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [56×12 char]
              fullpath: [56×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×56 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [56×12 char]
              fullpath: [56×119 char]
           files_exist: [56×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 4 participants, size is now 56

Pain :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Pain :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 14:48:13 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.010288 Image 1 66 contig. clusters, sizes 1 to 19226 Positive effect: 19808 voxels, min p-value: 0.00000000 Negative effect: 736 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 14:48:15 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×66 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 340 voxels displayed, 20204 not displayed on these slices

sagittal montage: 288 voxels displayed, 20256 not displayed on these slices

sagittal montage: 340 voxels displayed, 20204 not displayed on these slices

axial montage: 3361 voxels displayed, 17183 not displayed on these slices

axial montage: 3719 voxels displayed, 16825 not displayed on these slices

ans =

fmridisplay with properties: overlay: '/Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img' SPACE: [1×1 struct] activation_maps: {[1×1 struct]} montage: {[1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct] [1×1 struct]} surface: {} orthviews: {} history: {} history_descrip: [] additional_info: ''

drawnow, snapnow;

Vicarious cue

clear all

Vicarious :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0022.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0022.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 5.901843 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5790161  Bit rate: 22.47 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Vicarious :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 14:48:30 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

Vicarious :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 33.33% Expected 3.00 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 1 images Cases 14 Uncorrected: 5 images Cases 10 13 14 35 38 Retained 13 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 53.33% Expected 3.00 outside 95% ellipsoid, found 1 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 1 images Cases 60 Mahalanobis (cov and corr, q<0.05 corrected): 1 images Outlier_count Percentage _____________ __________ global_mean 3 5 global_mean_to_variance 1 1.6667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.6667 mahal_cov_uncor 5 8.3333 mahal_cov_corrected 1 1.6667 mahal_corr_uncor 1 1.6667 mahal_corr_corrected 0 0 Overall_uncorrected 6 10 Overall_corrected 2 3.3333

SPM12: spm_check_registration (v7759) 14:48:53 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Vicarious :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

Vicarious :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
  %  disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×58 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [58×12 char]
              fullpath: [58×119 char]
           files_exist: [58×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 2 participants, size is now 58

Vicarious :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Vicarious :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 14:48:57 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.003373 Image 1 114 contig. clusters, sizes 1 to 2037 Positive effect: 4582 voxels, min p-value: 0.00000000 Negative effect: 2155 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 14:48:58 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×114 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 204 voxels displayed, 6533 not displayed on these slices

sagittal montage: 59 voxels displayed, 6678 not displayed on these slices

sagittal montage: 166 voxels displayed, 6571 not displayed on these slices

axial montage: 1187 voxels displayed, 5550 not displayed on these slices

axial montage: 1343 voxels displayed, 5394 not displayed on these slices

ans =

drawnow, snapnow;

Cognitive cue

Cognitive :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0023.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0023.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 6.042847 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5791224  Bit rate: 22.47 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

Cognitive :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 14:49:16 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

Cognitive :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 35.00% Expected 3.00 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 8 images Cases 3 7 13 21 23 26 30 35 Retained 13 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 46.67% Expected 3.00 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 0 images Outlier_count Percentage _____________ __________ global_mean 2 3.3333 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.6667 mahal_cov_uncor 8 13.333 mahal_cov_corrected 0 0 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 8 13.333 Overall_corrected 1 1.6667

SPM12: spm_check_registration (v7759) 14:49:36 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

Cognitive :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

Cognitive :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
   % disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [59×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [59×119 char]
           files_exist: [59×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 1 participants, size is now 59

Cognitive :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

Cognitive :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 14:49:40 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.001855 Image 1 64 contig. clusters, sizes 1 to 2290 Positive effect: 1962 voxels, min p-value: 0.00000000 Negative effect: 1742 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 14:49:41 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×64 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 133 voxels displayed, 3571 not displayed on these slices

sagittal montage: 114 voxels displayed, 3590 not displayed on these slices

sagittal montage: 115 voxels displayed, 3589 not displayed on these slices

axial montage: 773 voxels displayed, 2931 not displayed on these slices

axial montage: 846 voxels displayed, 2858 not displayed on these slices

ans =

drawnow, snapnow;

General :: load dataset

mount_dir = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel';
con_list = dir(fullfile(mount_dir, '*/con_0024.nii'));
spm('Defaults','fMRI') 
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
sampleto = '/Volumes/spacetop_projects_social/analysis/fmri/spm/univariate/model-02_CcEScsA_24dofcsd/1stLevel/sub-0007/con_0024.nii'
loading mask. mapping volumes. 
checking that dimensions and voxel sizes of volumes are the same. 
Pre-allocating data array. Needed: 23960880 bytes
Loading image number:    60
Elapsed time is 6.089119 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 5793764  Bit rate: 22.47 bits
contrast_name = {'cue_P', 'cue_V', 'cue_C',...
    'cueXcue_P', 'cueXcue_V', 'cueXcue_C',...
    'stim_P', 'stim_V', 'stim_C',...
    'stimXcue_P', 'stimXcue_V', 'stimXcue_C',...
    'stimXint_P', 'stimXint_V', 'stimXint_C',...
    'motor', ...
    'simple_cue_P', 'simple_cue_V', 'simple_cue_C','simple_cue_G',...
    'simple_cueXcue_P', 'simple_cueXcue_V', 'simple_cueXcue_C','simple_cueXcue_G', ...
    'simple_stim_P', 'simple_stim_V', 'simple_stim_C','simple_stim_G',...
    'simple_stimXcue_P', 'simple_stimXcue_V', 'simple_stimXcue_C','simple_stimXcue_G',...
    'simple_stimXint_P', 'simple_stimXint_V','simple_stimXint_C', 'simple_stimXint_G'};

General :: check data coverage

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 14:49:58 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

General :: Plot diagnostics, before l2norm

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj)

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 31.67% Expected 3.00 outside 95% ellipsoid, found 8 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 8 images Cases 7 10 14 21 30 31 38 47 Retained 17 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 45.00% Expected 3.00 outside 95% ellipsoid, found 3 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 3 images Cases 8 17 60 Mahalanobis (cov and corr, q<0.05 corrected): 0 images Outlier_count Percentage _____________ __________ global_mean 2 3.3333 global_mean_to_variance 1 1.6667 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 1 1.6667 mahal_cov_uncor 8 13.333 mahal_cov_corrected 0 0 mahal_corr_uncor 3 5 mahal_corr_corrected 0 0 Overall_uncorrected 11 18.333 Overall_corrected 1 1.6667

SPM12: spm_check_registration (v7759) 14:50:17 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

wh_outlier_uncorr = 60×1 logical array

wh_outlier_corr = 60×1 logical array

General :: run robfit

set(gcf,'Visible','on')
figure ('Visible', 'on');
drawnow, snapnow;

General :: remove outliers based on plot

con = con_data_obj;
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))))
current length is 60
%for s = 1:length(wh_outlier_corr)
    %disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [60×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [59×119 char]
           files_exist: [60×1 logical]
               history: {1×4 cell}
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:)
con = 
  fmri_data with properties:

          source_notes: 'Info about image source here'
                     X: []
                  mask: [1×1 fmri_mask_image]
          mask_descrip: 'REMOVED: CHANGED SPACE'
    images_per_session: []
                     Y: []
               Y_names: []
             Y_descrip: 'Behavioral or outcome data matrix.'
            covariates: []
       covariate_names: {''}
    covariates_descrip: 'Nuisance covariates associated with data'
       history_descrip: 'Cell array of names of methods applied to this data, in order'
       additional_info: [0×0 struct]
        metadata_table: [0×0 table]
                   dat: [99837×59 single]
           dat_descrip: []
               volInfo: [1×1 struct]
        removed_voxels: 0
        removed_images: 0
           image_names: [59×12 char]
              fullpath: [59×119 char]
           files_exist: [59×1 logical]
               history: {1×4 cell}
%end
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))))
after removing 1 participants, size is now 59

General :: plot diagnostics, after l2norm

imgs2 = con.rescale('l2norm_images');

General :: ttest

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t)

SPM12: spm_check_registration (v7759) 14:50:21 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×1 struct}

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.008587 Image 1 117 contig. clusters, sizes 1 to 13443 Positive effect: 13198 voxels, min p-value: 0.00000000 Negative effect: 3947 voxels, min p-value: 0.00000000

orthviews(fdr_t)

SPM12: spm_check_registration (v7759) 14:50:22 - 01/06/2022 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

ans = 1×1 cell array

{1×117 struct}

drawnow, snapnow;

create_figure('montage'); axis off

montage(fdr_t)

Setting up fmridisplay objects

sagittal montage: 474 voxels displayed, 16671 not displayed on these slices

sagittal montage: 461 voxels displayed, 16684 not displayed on these slices

sagittal montage: 419 voxels displayed, 16726 not displayed on these slices

axial montage: 2748 voxels displayed, 14397 not displayed on these slices

axial montage: 3296 voxels displayed, 13849 not displayed on these slices

ans =

drawnow, snapnow;